Integration of Data Driven Technologies in Smart Grids for Resilient and Sustainable Smart Cities: A Comprehensive Review

A modern-day society demands resilient, reliable, and smart urban infrastructure for effective and in telligent operations and deployment. However, unexpected, high-impact, and low-probability events such as earthquakes, tsunamis, tornadoes, and hurricanes make the design of such robust infrastructure more complex. As a result of such events, a power system infrastructure can be severely affected, leading to unprecedented events, such as blackouts. Nevertheless, the integration of smart grids into the existing framework of smart cities adds to their resilience. Therefore, designing a resilient and reliable power system network is an inevitable requirement of modern smart city infras tructure. With the deployment of the Internet of Things (IoT), smart cities infrastructures have taken a transformational turn towards introducing technologies that do not only provide ease and comfort to the citizens but are also feasible in terms of sustainability and dependability. This paper presents a holistic view of a resilient and sustainable smart city architecture that utilizes IoT, big data analytics, unmanned aerial vehicles, and smart grids through intelligent integration of renew able energy resources. In addition, the impact of disasters on the power system infrastructure is investigated and different types of optimization techniques that can be used to sustain the power flow in the network during disturbances are compared and analyzed. Furthermore, a comparative review analysis of different data-driven machine learning techniques for sustainable smart cities is performed along with the discussion on open research issues and challenges

Energy Efficient Distributed Processing for IoT

The number of connected objects in the Internet of Things (IoT) is growing exponentially. IoT devices are expected to number between 26 billion to 50 billion devices by 2020 and this figure can grow even further due to the production of miniaturised portable devices that are lightweight, energy and cost efficient together with the widespread use of the Internet and the added value organisations and individuals can gain from IoT devices, if their data is processed. These connected objects are expected to be used in multitudes of applications, of which, some are, highly resource intensive such as visual processing services for surveillance based object recognition applications. The sensed data requires processing by the cloud in order to extract knowledge and make decisions accordingly. Given the pervasiveness of future IoT-based visual processing applications, massive amounts of data will be collected due to the nature of multimedia files. Transporting all that collected data to the cloud at the core of the network, is prohibitively costly, in terms of energy consumption. Hence, to tackle the aforementioned challenges, distributed processing is proposed by academia and industry to make use of a large number of devices located in the edge of the network to process some or all of the data before it gets to the cloud. Due to the heterogeneity of the devices in the edge of the network, it is crucial to develop energy efficient models that take care of resource provisioning optimally. The focus in today’s network design and development has shifted towards energy efficiency, due to the rising cost of electricity, resource scarcity and increasing emission of carbon dioxide (CO2). This thesis addresses some of the challenges associated with service placement in a distributed architecture such as the fog. First, a Passive Optical Network (PON) is used to connect IoT devices and to support the fog infrastructure. A metro network is also used to connect to the fog and aggregate traffic from the PON towards the core network. An IP/WDM backbone network is considered to model the core layer and to interconnect the cloud data centres. The entire network was modelled and optimised through Mixed Integer Linear Programming (MILP) and the total end to end power consumption was jointly minimised for processing and networking. Two aspects of service placements were examined: 1) non-splitable services, and 2) splitable services. The results obtained showed that, in the capacitated problem, service splitting introduced power consumption savings of up to 86% compared to 46% with non-splitable services. Moreover, an energy efficient special purposed data centre (SP-DC) was deployed in addition to its general purpose counterpart (GP-DC). The results showed that, for very high demands, power savings of up to 50% could be achieved compared to 30% without SP-DC. The performance of the proposed architecture was further examined by considering additional dimensions to the problem of service placements such as resiliency dimension in terms of 1+1 server protection, in the long term network design problem (un-capacitated) and the impact of inter-service synchronisation overhead on the total number service splits per task

INQUIRIES IN INTELLIGENT INFORMATION SYSTEMS: NEW TRAJECTORIES AND PARADIGMS

Rapid Digital transformation drives organizations to continually revitalize their business models so organizations can excel in such aggressive global competition. Intelligent Information Systems (IIS) have enabled organizations to achieve many strategic and market leverages. Despite the increasing intelligence competencies offered by IIS, they are still limited in many cognitive functions. Elevating the cognitive competencies offered by IIS would impact the organizational strategic positions. With the advent of Deep Learning (DL), IoT, and Edge Computing, IISs has witnessed a leap in their intelligence competencies. DL has been applied to many business areas and many industries such as real estate and manufacturing. Moreover, despite the complexity of DL models, many research dedicated efforts to apply DL to limited computational devices, such as IoTs. Applying deep learning for IoTs will turn everyday devices into intelligent interactive assistants. IISs suffer from many challenges that affect their service quality, process quality, and information quality. These challenges affected, in turn, user acceptance in terms of satisfaction, use, and trust. Moreover, Information Systems (IS) has conducted very little research on IIS development and the foreseeable contribution for the new paradigms to address IIS challenges. Therefore, this research aims to investigate how the employment of new AI paradigms would enhance the overall quality and consequently user acceptance of IIS. This research employs different AI paradigms to develop two different IIS. The first system uses deep learning, edge computing, and IoT to develop scene-aware ridesharing mentoring. The first developed system enhances the efficiency, privacy, and responsiveness of current ridesharing monitoring solutions. The second system aims to enhance the real estate searching process by formulating the search problem as a Multi-criteria decision. The system also allows users to filter properties based on their degree of damage, where a deep learning network allocates damages in 12 each real estate image. The system enhances real-estate website service quality by enhancing flexibility, relevancy, and efficiency. The research contributes to the Information Systems research by developing two Design Science artifacts. Both artifacts are adding to the IS knowledge base in terms of integrating different components, measurements, and techniques coherently and logically to effectively address important issues in IIS. The research also adds to the IS environment by addressing important business requirements that current methodologies and paradigms are not fulfilled. The research also highlights that most IIS overlook important design guidelines due to the lack of relevant evaluation metrics for different business problems

Internet of Nano-Things, Things and Everything: Future Growth Trends

The current statuses and future promises of the Internet of Things (IoT), Internet of Everything (IoE) and Internet of Nano-Things (IoNT) are extensively reviewed and a summarized survey is presented. The analysis clearly distinguishes between IoT and IoE, which are wrongly considered to be the same by many commentators. After evaluating the current trends of advancement in the fields of IoT, IoE and IoNT, this paper identifies the 21 most significant current and future challenges as well as scenarios for the possible future expansion of their applications. Despite possible negative aspects of these developments, there are grounds for general optimism about the coming technologies. Certainly, many tedious tasks can be taken over by IoT devices. However, the dangers of criminal and other nefarious activities, plus those of hardware and software errors, pose major challenges that are a priority for further research. Major specific priority issues for research are identified

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI

Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the computing paradigms, i.e., cloud computing and edge computing. In recent years, we have witnessed significant progress in developing more advanced AI models on cloud servers that surpass traditional deep learning models owing to model innovations (e.g., Transformers, Pretrained families), explosion of training data and soaring computing capabilities. However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed. In this survey, we conduct a systematic review for both cloud and edge AI. Specifically, we are the first to set up the collaborative learning mechanism for cloud and edge modeling with a thorough review of the architectures that enable such mechanism. We also discuss potentials and practical experiences of some on-going advanced edge AI topics including pretraining models, graph neural networks and reinforcement learning. Finally, we discuss the promising directions and challenges in this field.Comment: 20 pages, Transactions on Knowledge and Data Engineerin

LPWA-based IoT Technology Selection for Smart Metering Deployment in Urban and Sub Urban Areas: A State Electricity Company Perspective

The need for LPWA-based Internet of Things (IoT) technology for deploying smart metering services is rapidly growing for its ability to manage energy usage in real-time and increase efficiency. However, the problem faced by electric utility companies is how to choose the most appropriate technology. This study uses a techno-economic approach to compare the two most widely used technological alternatives, namely establishing LoRaWAN as a non-licensed LPWA technology or leasing NB-IoT as a licensed LPWA technology owned by a telecommunications operator. Case studies conducted in the urban area of Bandung and sub-urban city of Tasikmalaya as an example of a typical town in Indonesia. The results showed that LoRaWAN and NB-IoT are both technically and business feasible to be implemented with their respective advantages. LoRaWAN is superior in battery lifetime, business model, speed of implementation, and total costs, whereas NB-IoT is superior in range, capacity, quality of service, security, and ecosystem support. Using PLN's perspective as a national electricity company in Indonesia, LoRaWAN has a Net Present Value of 23% higher than NB-IoT in the 10th year

Digital Technologies as Enablers of Component Reuse : Value Chain Perspectives in Construction & Manufacturing

Our planet is experiencing climate emergency due to the overconsumption of natural resources and ever-increasing carbon footprint. The construction and manufacturing industries are by far the biggest contributors to this grim situation. Hence, it is of paramount importance that the current economic model in those industries shifts from conventional linear to circular. Among the different circular economy (CE) approaches, adopting the component reuse practices is more imperative; because, after reduce, reuse is considered to be the least resource and energy intensive CE principle. With regard to transformation of the construction and manufacturing industries towards component reuse, digitalization could play a major enabling role. However, how the digital technologies such as BIM, digital twin, IoT (sensors and RFIDs), and robots could facilitate the component reuse practices is still an underexplored field of study. Additionally, the studies thus far in this direction lack the integrative approach both from multi-technology and multi-stakeholder perspectives. Therefore, the objective of this research is to investigate the perspectives of value chain actors, in construction and manufacturing, on how the digital technologies can advance component reuse practices. To address the research objective, this study employs qualitative research methodology and therein, multiple case study method. For the selection of most relevant cases, purposive sampling strategy was used. As a result, ten cases were selected, out of which, six are from the construction industry and the remaining four belong to manufacturing industry. To garner the primary data from those cases, semi-structured elite interviews were carried out. Subsequently, the data analysis process proceeded from within-case analysis to cross-case analysis. Finally, the findings from construction industry were juxtaposed to the findings from manufacturing industry, in order to examine the similarities and differences in how the digital technologies can advance component reuse practices in each industry. The findings of this study suggest that both the construction and manufacturing industries are becoming more perceptive to the need circular economy transformation. They recognize that the digital technologies are de facto the cornerstones in their efforts to adopt component reuse practices. The results demonstrate that collectively the BIM and IoT in construction, similar to digital twin and IoT in manufacturing, enables several component reuse practices- namely, DfDR, predictive maintenance, logistics & inventory management, quality & lifecycle assessment, and component disassembly planning. In addition, a few digital technology-enabled reuse practices were identified, that are peculiar to each industry. Robots, for instance, were recognized for the potential to partially automate some repetitive processes in construction industry, but that was not the case in manufacturing. Nevertheless, this study indicate that, for the technologies to be optimal in their enabling role, their current technological capabilities need to be developed further in the future. This study enriches the literature stream in circular economy and digitalization both in terms research methodology and findings. By taking a broader and integrative stance and through comparative study of two industries, this study validates several previous findings and also pro-poses novel findings of its own. To the practitioners the findings will provide comprehensive in-sights that may be useful in their efforts to adopt or foster digitalization in component reuse context. Finally, this study identifies a few directions for future research that may result in promising outcomes